When studying treatment effects in multilevel studies, investigators commonly use (semi-)parametric estimators, which make strong parametric assumptions about the outcome, the treatment, and/or the correlation between individuals. We propose two nonparametric, doubly robust, asymptotically Normal estimators of treatment effects that do not make such assumptions. The first estimator is an extension of the cross-fitting estimator applied to clustered settings. The second estimator is a new estimator that uses conditional propensity scores and an outcome covariance model to improve efficiency. We apply our estimators in simulation and empirical studies and find that they consistently obtain the smallest standard errors.

翻译：在研究多层次研究的治疗效果时,调查人员通常使用(半)参数参数估测器,这些估测器对结果、治疗和(或)个人之间的关系作出强烈的参数假设。我们建议使用两个非对称的、双重强健的、无常态的、不作这种假设的治疗效果估计器。第一个估测器是适用于集群环境的交叉估计值的延伸。第二个估测器是一个新的估测器,使用有条件的倾向性分数和结果共变模型来提高效率。我们在模拟和经验研究中应用我们的估测器,发现他们一贯获得最小的标准差错。